When studying how humans interact with computing devices, it is desirable to be able to determine the effectiveness of a computer-user interface, i.e., a user interface. A traditional way of determining the effectiveness of a user interface is to present a computer user, i.e., a user, with a task, observe the user as he or she operates the user interface to complete the task, and ask the user questions before, during, and/or after the task is performed. The observed behavior and answers to the questions are characterized and quantified. The quantified results are analyzed to determine the effectiveness of the user interface.
Cognitive neuroscience techniques can be used to provide a more direct way to determine the effectiveness of user interfaces. A typical cognitive neuroscience technique involves attaching electrical sensors, i.e., sensors, to various points on a user's scalp. The sensors are then connected to an electroencephalograph (EEG). One use of an EEG is to sense electrical changes within the brain that correspond to certain brain states. It is possible to determine the effectiveness of a user interface by analyzing a user's brain states before, during, and/or after a user performs a task using the user interface.
As the name implies “cognitive” neuroscience techniques, also called cognitive techniques, are focused on thought processes. Cognitive techniques analyze electrical signals caused by electrical changes within the brain, i.e., EEG signals. The EEG signals contain data and patterns of data associated with brain states, which can be used to infer the existence of thought processes. A problem with cognitive techniques is that EEG signals often contain artifacts, i.e., unwanted data, that may distort the brain state information. In the past, attempts have been made to overcome artifacts or “noise problems” by filtering EEG signals and by using neurofeedback. While effective at eliminating some artifacts, filtering and neurofeedback add significant cost and complexity to cognitive techniques which in turn reduces the utility of applying cognitive techniques to determine the effectiveness of user interfaces.